High-resolution hydrodynamic zoom-in simulations of Milky Way-mass halos offer exquisite resolution and provide insights into the small-scale challenges associated with cold dark matter. However, these simulations are computationally expensive, so studying a diverse sample of simulated Milky Way analogs is currently infeasible. We present a machine learning model trained on simulations from the Feedback in Realistic Environments project that efficiently predicts surviving subhalo populations from dark-matter-only simulations, and we show that the predicted subhalo populations agree well with hydrodynamic results. We discuss several applications of this technique, including its use in modeling satellite galaxy populations around Milky Way analogs and around the Milky Way itself.
Speaker: Ethan Nadler, Stanford
Editor's Note: This talk is also being given on 4/24 at UC Berkeley
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